Advantages and Drawbacks of Open-Ended, Use-Agnostic Citizen Science Data Collection: A Case Study
Bibliographic record
Abstract
Citizen science projects that collect natural history observations often do not have an underlying research question in mind. Thus, data generated from such projects can be considered “use-agnostic.” Nevertheless, such projects can yield important insights about species distributions. Many of these projects use a class-based data schema, whereby contributors must supply a species identification. This can limit participation if contributors are not confident in their identifications, and can introduce data quality issues if species identification is incorrect. Some projects, such as iNaturalist, circumvent this with crowdsourced species identifications based on contributed photographs, or by grading confidence in the data based on attributes of the sighting and/or contributor. An alternative to a class-based data schema is an open-ended (instance-based) one, where contributors are free to identify their sighting at whatever taxonomic resolution they are most confident, and/or describe the sighting based on attributes. This can increase participation (data completeness) and have the benefit of adding additional (and sometimes unexpected) information. The regionally-focused citizen science website NLNature.com was designed to experimentally examine how class-based versus instance-based schema affected contributions and data quality. Here, we show that the instance-based schema yielded not only more contributions, but also several of ecological importance. Thus, allowing contributors to supply natural history information at a level familiar to them increases data completeness and facilitates unanticipated contributions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.106 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".